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Deyi Zhao Miran Tang Zhexiao Ma Panjie Hu Qingxia Fu Zhuocheng Yao Cui Zhou Tieli Zhou Jianming Cao a School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, Chinab Department of Clinical Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Chinac Key Laboratory of Clinical Laboratory Diagnosis and Translational Research of Zhejiang Province, Wenzhou, Zhejiang, China
Virulence, 21.10.2024
Tilføjet 21.10.2024
Garland, Allan; Li, Na; Sligl, Wendy; Lane, Alana; Thavorn, Kednapa; Wilcox, M. Elizabeth; Rochwerg, Bram; Keenan, Sean; Marrie, Thomas J.; Kumar, Anand; Curley, Emily; Ziegler, Jennifer; Dodek, Peter; Loubani, Osama; Gervais, Alain; Murthy, Srinivas; Neto, Gina; Prescott, Hallie C.; for the Sepsis Canada Network
Critical Care Medicine, 21.10.2024
Tilføjet 21.10.2024
Objectives: Refine the administrative data definition of sepsis in hospitalized patients, including less severe cases. Design and Setting: For each of 1928 infection and 108 organ dysfunction codes used in Canadian hospital abstracts, experts reached consensus on the likelihood that it could relate to sepsis. We developed a new algorithm, called AlgorithmL, that requires at least one infection and one organ dysfunction code adjudicated as likely or very likely to be related to sepsis. AlgorithmL was compared with four previously described algorithms, regarding included codes, population-based incidence, and hospital mortality rates—separately for ICU and non-ICU cohorts in a large Canadian city. We also compared sepsis identification from these code-based algorithms with the Centers for Disease Control’s Adult Sepsis Event (ASE) definition. Subjects: Among Calgary’s adult population of 1.033 million there were 61,632 eligible hospitalizations. Interventions: None. Measurements and Main Results: AlgorithmL includes 720 infection codes and 50 organ dysfunction codes. Comparison algorithms varied from 42–941 infection codes to 2–36 organ codes. There was substantial nonoverlap of codes in AlgorithmL vs. the comparators. Annual sepsis incidence rates (per 100,000 population) based on AlgorithmL were 91 in the ICU and 291 in the non-ICU cohort. Incidences based on comparators ranged from 28–77 for ICU to 11–266 for non-ICU cohorts. Hospital sepsis mortality rates based on AlgorithmL were 24% in ICU and 17% in non-ICU cohorts; based on comparators, they ranged 27–38% in the ICU cohort and 18–47% for the non-ICU cohort. Of AlgorithmL-identified cases, 41% met the ASE criteria, compared with 42–82% for the comparator algorithms. Conclusions: Compared with other code-based algorithms, AlgorithmL includes more infection and organ dysfunction codes. AlgorithmL incidence rates are higher; hospital mortality rates are lower. AlgorithmL may more fully encompass the full range of sepsis severity.
Læs mere Tjek på PubMedChalisa Pinitchun, Wimonrat Panpetch, Thansita Bhunyakarnjanarat, Kanyarat Udompornpitak, Huy Thanh Do, Peerapat Visitchanakun, Dhammika Leshan Wannigama, Suwasin Udomkarnjananun, Monruedee Sukprasansap, Tewin Tencomnao, Pattarin Tangtanatakul, Asada Leelahavanichkul
PLoS One Infectious Diseases, 19.10.2024
Tilføjet 19.10.2024
by Chalisa Pinitchun, Wimonrat Panpetch, Thansita Bhunyakarnjanarat, Kanyarat Udompornpitak, Huy Thanh Do, Peerapat Visitchanakun, Dhammika Leshan Wannigama, Suwasin Udomkarnjananun, Monruedee Sukprasansap, Tewin Tencomnao, Pattarin Tangtanatakul, Asada Leelahavanichkul Introduction Despite the well-established effects of aging on brain function and gut dysbiosis (an imbalance in gut microbiota), the influence of aging on sepsis-associated encephalopathy (SAE) and the role of probiotics in this context remain less understood. Methods C57BL/6J mice (8-week-old) were subcutaneously administered with 8 weeks of D-galactose (D-gal) or phosphate buffer solution (PBS) for aging and non-aging models, respectively, with or without 8 weeks of oral Lacticaseibacillus rhamnosus GG (LGG). Additionally, the impact of the condition media from LGG (LCM) was tested in macrophages (RAW 264.7 cells), microglia (BV-2 cells), and hippocampal cells (HT-22 cells). Result Fecal microbiome analysis demonstrated D-gal-induced dysbiosis (reduced Firmicutes and Desulfobacterota with increased Bacteroidota and Verrucomicrobiota), which LGG partially neutralized the dysbiosis. D-gal also worsens cecal ligation and puncture (CLP) sepsis severity when compared with PBS-CLP mice, as indicated by serum creatinine (Scr) and alanine transaminase (ALT), but not mortality, neurological characteristics (SHIRPA score), and serum cytokines (TNF-α and IL-6). Additionally, D-gal-induced aging was supported by fibrosis in the liver, kidney, and lung; however, CLP sepsis did not worsen fibrosis. Interestingly, LGG attenuated all parameters (mortality, Scr, ALT, SHIRPA, and cytokines) in non-aging sepsis (PBS-CLP) while improving all these parameters, except for mortality and serum IL-6, in aging sepsis (D-gal CLP). For the in vitro test using lipopolysaccharide (LPS) stimulation, LCM attenuated inflammation in some parameters on RAW264.7 cells but not BV-2 and HT-22 cells, implying a direct anti-inflammatory effect of LGG on macrophages, but not in cells from the brain. Conclusion D-gal induced fecal dysbiosis and worsened sepsis severity as determined by Scr and ALT, and LGG could alleviate most of the selected parameters of sepsis, including SAE. However, the impact of LGG on SAE was not a direct delivery of beneficial molecules from the gut to the brain but partly due to the attenuation of systemic inflammation through the modulation of macrophages.
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